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4.
RDF, the good parts...
RDF as an integration language
RDF as a lingua franca for semantic web and linked data
RDF data stores & SPARQL
RDF flexibility
Data can be adapted to multiple environments
Open and reusable data by default

6.
Why describe & validate RDF?
For RDF producers
Developers can understand the contents they are going to produce
They can ensure they produce the expected structure
Advertise the structure
Generate interfaces
For RDF consumers
Understand the contents
Verify the structure before processing it
Query generation & optimization

10.
Understanding the problem
Shapes ≠ types
Nodes in RDF graphs can have zero, one or many rdf:type arcs
One type can be used for multiple purposes (foaf:Person)
Data doesn't need to be annotated with fully discriminating types
foaf:Person can represent friend, invitee, patient,...
Different meanings and different structure depending on the context
We should be able to define specific validation constraints in different contexts

11.
Understanding the problem
RDF flexibility
Mixed use of objects & literals
schema:creator can be a string or schema:Person in the same data
:angie schema:creator "Keith Richards" ,
[ a schema:Person ;
schema:singleName "Mick" ;
schema:lastName "Jagger"
] .
See other examples from http://schema.org

12.
Understanding the problem
Repeated properties
Sometimes, the same property is used for different purposes in the
same data
Example: A book record must have 2 codes with different structure
:book schema:productID "isbn:123-456-789";
schema:productID "code456" .
A practical example from FHIR
See: http://hl7-fhir.github.io/observation-example-bloodpressure.ttl.html